Data valorization and robust optimization to guide the energy transition towards smart grids with a strong renewable component (VORTEX)

Bringing together world-renowned experts in operational research, this project proposes a complex linkage of models operating at increasing time scales (day, week, month, years) and other mathematical tools (machine learning, robust optimization) for reasoned management of the transition to smart electricity grids with a high renewable component. Demand response becomes an essential component of the system for smoothing supply-demand imbalances, but complex to model as it involves the individual decisions of a very large number of agents. It collaborates to the development of a long-term prospective analysis tool (ETEM-SG) for the regional energy systems of the Montréal Metropolitan Community, with options offered by smart grids including demand response.